Deep learning of feature representation with multiple instance learning for medical image analysis

By Yan Xu, Tao Mo, Qiwei Feng, Peilin Zhong, Maode Lai, Eric I-Chao Chang. IEEE International Conference on Acoustics, Speech, and Signal Processing, 2014

415 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Oncology

View Paper: https://www.semanticscholar.org/paper/a7897e61633a97e648057bee95c082c1aab40d41

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In Computer Vision and Pattern Recognition

In Oncology

Citations in the corpus

Using Non-invertible Data Transformations to Build Adversary-Resistant Deep Neural Networks (2016, background): “Recent interest has been to integrate it into critical fields like medical imaging [1], [37] and self-driving cars [13], [8].”
Weakly Supervised Deep Learning for Whole Slide Lung Cancer Image Analysis (2020, background): “also extensively studied in these MIL methods [44]–[46], which actually require considerable efforts to design and validate.”
Learning Adversary-Resistant Deep Neural Networks (2016, neutral): “Recent explorations and applications include those in medical imaging [1, 38] and self-driving cars [11, 14].”
Hierarchical and Robust Convolutional Neural Network for Very High-Resolution Remote Sensing Object Detection (2019, background): “Nonetheless, manual features cannot provide effective expressions of the semantic information [12]–[14], [21].”
Blockchain for AI: Review and Open Research Challenges (2019, methodology): “have been extensively used for medical image analysis to achieve a high level of accuracy [121].”
Boosting Deep Learning Risk Prediction with Generative Adversarial Networks for Electronic Health Records (2017, background): “…of excellent work have been conducted in seek of novel deep learning solutions in different healthcare applications including but not limited to computational phenotyping [9], [10], risk prediction [11], [12], medical imaging analysis [13], [14], and clinical natural language processing [15].”
Lung Cancer Detection and Classification with 3D Convolutional Neural Network (3D-CNN) (2017, methodology): “Because the Kaggle dataset alone proved to be inadequate to accurately classify the validation set, we also use the patient lung CT scan dataset with labeled nodules from the LUng Nodule Analysis 2016 (LUNA16) Challenge [7] to train a U-Net for lung nodule detection.”
Radiology Objects in COntext (ROCO): A Multimodal Image Dataset (2018, methodology): “As deep learning techniques [9] have improved prediction accuracies in image classification [8] and in applications such as medical imaging [24], a deep learning architecture is used to filter the extracted PMC subset and is described in Subsection 4.”
I Lead, You Help but Only with Enough Details: Understanding User Experience of Co-Creation with Artificial Intelligence (2018, background): “AI is expected to become increasingly prevalent in numerous areas [3, 6, 16, 59].”
Diagnose like a Radiologist: Attention Guided Convolutional Neural Network for Thorax Disease Classification (2018, background): “The noticeable progress in deep learning has beneﬁted many trials in medical image analysis, such as lesion segmentation or detection [1], [2], [3], [4], [5], diseases classiﬁcation [6], [7], [8], [9], noise induction [10], image annotation [11], [12], registration [13], regression [14] and so on.”
Deep Multi-patch Aggregation Network for Image Style, Aesthetics, and Quality Estimation (2015, methodology): “In [35] and [14], deep learning features were incorporated into a MIL framework, and used to perform medical image analysis and object detection, respectively.”
Deep multiple instance learning for image classification and auto-annotation (2015, methodology): “Recently, researchers started to study weakly supervised learning using features learned with deep representations [47, 35].”
Patch-based Convolutional Neural Network for Whole Slide Tissue Image Classification (2015, methodology): “Utilizing unlabeled patches has led to Multiple Instance Learning (MIL) based WSI classification [16, 51, 52].”
Using Non-invertible Data Transformations to Build Adversarial-Robust Neural Networks (2016, background): “Recent interest has been to integrate it into critical ﬁelds like medical imaging [1], [37] and self-driving cars [13], [8].”
A survey on deep learning in medical image analysis (2017, methodology): “Xu et al. (2014) investigated the use of a MIL-framework with both supervised and unsu-9 pervised feature learning approaches as well as hand-crafted features.”
Learning Adversary-Resistant Deep Neural Networks (2016, background): “clude those in medical imaging [2], [38] and self-driving cars [10], [14].”
Lung Nodule Classification Using Deep Features in CT Images (2015, methodology): “Xu Yan et al. [10] present the effectiveness of using deep neural networks (DNN) for feature extraction in medical image analysis and report that the performance of an automatic unsupervised approach for feature extraction from DNN is as effective as a supervised approach.”